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MSGAN: multi-stage generative adversarial network-based data recovery in cyber-attacks

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Abstract

In an industrial control system, a programmable logic controller (PLC) plays a vital role in maintaining the stable operation of the system. Cyber-attacks can affect the regular operation by tampering with the data stored in the PLC, thereby damaging to the system. Thus, it is particularly important to develop an efficient cyber-attacks recovery method. However, owing to the impact of unknown factors in theoretical methods, poor scalability of automaton theory, and a lack of constraints during the training process of deep learning network models, the restoration accuracy and stability are low. Therefore, it is a significant challenge to design an appropriate method to improve the accuracy and stability of cyber-attacks recovery. In this study, the generative adversarial networks were applied to the problem of cyber-attacks recovery; furthermore, a multi-stage generative adversarial networks was designed. The model consisted of a Variational Autoencoder and two conditional energy-based generative adversarial networks (CEBGANs). Then the second CEBGAN uses the fitted random noise appending with the data generated by the previous stage and the historical data as additional information to obtain the restoration results. Moreover, a self-adaptive decision policy was established to enhance the restoration accuracy and stability. Experimental results demonstrated that the proposed method in this manuscript could effectively improve the accuracy of cyber-attacks data recovery and reduce the possibility of outliers in data recovery.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Foundation item: Beijing Natural Science Foundation (No. L192020). National Key Research and Development Project (Key Technologies and Applications of Security and Trusted Industrial Control System No. 2020YFB2009500).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Bitao Tian, Yingxu Lai, Motong Sun, Yipeng Wang & Jing Liu

  2. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, 100124, China

    Yingxu Lai, Yipeng Wang & Jing Liu

Authors
  1. Bitao Tian
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  2. Yingxu Lai
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  3. Motong Sun
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  4. Yipeng Wang
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  5. Jing Liu
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Contributions

BT: conceptualization, methodology, investigation, data curation, writing—original draft, visualization, software, validation. YL: supervision, writing—review & editing, investigation, formal analysis. MS: validation, investigation, data curation, visualization. YW: formal analysis, supervision, writing—review & editing, project administration. JL: formal analysis, supervision, writing—review & editing, project administration.

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Correspondence to Yingxu Lai.

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Tian, B., Lai, Y., Sun, M. et al. MSGAN: multi-stage generative adversarial network-based data recovery in cyber-attacks. Neural Comput & Applic 35, 20675–20695 (2023). https://doi.org/10.1007/s00521-023-08791-8

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